本文提出了面向城市交通管理的数字孪生（DT）构建方法与应用。当前多数关于数字孪生的研究聚焦于其“眼睛”，即新兴的感知与传感技术，如目标检测与跟踪；而真正使数字孪生区别于传统仿真器的，是其“大脑”——即从所见所感中提取模式并作出明智决策的预测与决策能力。为提升城市交通管理价值，数字孪生需由人工智能驱动，并融合低时延、高带宽的传感与网络技术，即网络物理系统（CPS）。本文可为研究人员与实践者识别数字孪生开发中的挑战与机遇提供指引；成为跨学科对话的桥梁；并为挖掘数字孪生在多样化城市交通应用场景中的潜力提供路线图。

We present methods and applications for the development of digital twins (DT) for urban traffic management. While the majority of studies on the DT focus on its ``eyes," which is the emerging sensing and perception like object detection and tracking, what really distinguishes the DT from a traditional simulator lies in its ``brain," the prediction and decision making capabilities of extracting patterns and making informed decisions from what has been seen and perceived. In order to add value to urban transportation management, DTs need to be powered by artificial intelligence and complement with low-latency high-bandwidth sensing and networking technologies, in other words, cyberphysical systems. This paper can be a pointer to help researchers and practitioners identify challenges and opportunities for the development of DTs; a bridge to initiate conversations across disciplines; and a road map to exploiting potentials of DTs for diverse urban transportation applications.